Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market
Bayesian model detects COVID-19 impact on US stock market, identifying change points in industry portfolio dependencies.
What it examines
This paper develops a Bayesian multivariate stochastic volatility model to detect abrupt changes in the US stock market's dependence structure during the COVID-19 pandemic, focusing on cross-industry relationships.
What it concludes
The research offers valuable insights into the COVID-19 pandemic's impact on the US stock market, with potential applications in financial risk management and policy-making. Future research could explore scalability and incorporate smooth changes in dependence structures.
Evidence objects
The research offers valuable insights into the COVID-19 pandemic's impact on the US stock market, with potential applications in financial risk management and policy-making. Future research could explore scalability and incorporate smooth changes in dependence structures.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: Reliable estimates of volatility and correlation are fundamental in economics and finance for understanding the impact of macroeconomics events on the market and guiding future investments and policies. Dependence across financial returns is likely to be subject to sudden structural changes, especially in correspondence with major global events, such as the COVID-19 pandemic. In this work, we are… ▽ More Reliable estimates of volatility and correlation are fundamental in economics and finance for understanding the impact of macroeconomics events on the market and guiding future investments and policies. Dependence across financial returns is likely to be subject to sudden structural changes, especially in correspondence with major global events, such as the COVID-19 pandemic. In this work, we are interested in capturing abrupt changes over time in the dependence across US industry stock portfolios, over a time horizon that covers the COVID-19 pandemic. The selected stocks give a comprehensive picture of the US stock market. To this end, we develop a Bayesian multivariate stochastic volatility model based on a time-varying sequence of graphs capturing the evolution of the dependence structure. The model builds on the Gaussian graphical models and the random change points literature. In particular, we treat the number, the position of change points, and the graphs as object of posterior inference, allowing for sparsity in graph recovery and change point detection. The high dimension of the parameter space poses complex computational challenges. However, the model admits a hidden Markov model formulation. This leads to the development of an efficient computational strategy, based on a combination of sequential Monte-Carlo and Markov chain Monte-Carlo techniques. Model and computational development are widely applicable, beyond the scope of the application of interest in this work. △ Less
Source row: 381 · abstract type: unknown